Graph neural architecture search: A survey
BM Oloulade, J Gao, J Chen, T Lyu… - Tsinghua Science and …, 2021 - ieeexplore.ieee.org
In academia and industries, graph neural networks (GNNs) have emerged as a powerful
approach to graph data processing ranging from node classification and link prediction tasks …
approach to graph data processing ranging from node classification and link prediction tasks …
How powerful are spectral graph neural networks
Abstract Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) based on
graph signal filters. Some models able to learn arbitrary spectral filters have emerged …
graph signal filters. Some models able to learn arbitrary spectral filters have emerged …
Deep generative models for materials discovery and machine learning-accelerated innovation
AS Fuhr, BG Sumpter - Frontiers in Materials, 2022 - frontiersin.org
Machine learning and artificial intelligence (AI/ML) methods are beginning to have
significant impact in chemistry and condensed matter physics. For example, deep learning …
significant impact in chemistry and condensed matter physics. For example, deep learning …
Discovering invariant rationales for graph neural networks
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input
graph's features--rationale--which guides the model prediction. Unfortunately, the leading …
graph's features--rationale--which guides the model prediction. Unfortunately, the leading …
Graph information aggregation cross-domain few-shot learning for hyperspectral image classification
Most domain adaptation (DA) methods in cross-scene hyperspectral image classification
focus on cases where source data (SD) and target data (TD) with the same classes are …
focus on cases where source data (SD) and target data (TD) with the same classes are …
Learning invariant graph representations for out-of-distribution generalization
Graph representation learning has shown effectiveness when testing and training graph
data come from the same distribution, but most existing approaches fail to generalize under …
data come from the same distribution, but most existing approaches fail to generalize under …
Learning knowledge graph embedding with heterogeneous relation attention networks
Knowledge graph (KG) embedding aims to study the embedding representation to retain the
inherent structure of KGs. Graph neural networks (GNNs), as an effective graph …
inherent structure of KGs. Graph neural networks (GNNs), as an effective graph …
Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
Abstract Graph Neural Networks (GNNs) have achieved promising performance on a wide
range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over …
range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over …
Diffusion improves graph learning
J Gasteiger, S Weißenberger… - Advances in neural …, 2019 - proceedings.neurips.cc
Graph convolution is the core of most Graph Neural Networks (GNNs) and usually
approximated by message passing between direct (one-hop) neighbors. In this work, we …
approximated by message passing between direct (one-hop) neighbors. In this work, we …
Bernnet: Learning arbitrary graph spectral filters via bernstein approximation
Many representative graph neural networks, $ eg $, GPR-GNN and ChebNet, approximate
graph convolutions with graph spectral filters. However, existing work either applies …
graph convolutions with graph spectral filters. However, existing work either applies …